SoroushVahidi/scidocs huggingface dataset:
Privacy protection based access control scheme in cloud-based services
(2016)

With the rapid development of the computer technology, cloud-based services have become a hot topic. Cloud-based services not only provide users with convenience, but also bring many security issues. Therefore, the study of access control scheme to protect users' privacy in cloud environment is of great significance. In this paper, we present an access control system with privilege separation based on privacy protection (PS-ACS). In the PS-ACS scheme, we divide the users into personal domain (PSD) and public domain (PUD) logically. In the PSD, we set read and write access permissions for users respectively. The Key-Aggregate Encryption (KAE) is exploited to implement the read access permission which improves the access efficiency. A high degree of patient privacy is guaranteed simultaneously by exploiting an Improved Attribute-based Signature (IABS) which can determine the users' write access. For the users of PUD, a hierarchical attribute-based encryption (HABE) is applied to avoid the issues of single point of failure and complicated key distribution. Function and performance testing result shows that the PS-ACS scheme can achieve privacy protection in cloud-based services.
Input feature selection for classification problems
(2002)

Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected. In this paper, we propose two feature selection algorithms. The limitation of mutual information feature selector (MIFS) is analyzed and a method to overcome this limitation is studied. One of the proposed algorithms makes more considered use of mutual information between input attributes and output classes than the MIFS. What is demonstrated is that the proposed method can provide the performance of the ideal greedy selection algorithm when information is distributed uniformly. The computational load for this algorithm is nearly the same as that of MIFS. In addition, another feature selection algorithm using the Taguchi method is proposed. This is advanced as a solution to the question as to how to identify good features with as few experiments as possible. The proposed algorithms are applied to several classification problems and compared with MIFS. These two algorithms can be combined to complement each other's limitations. The combined algorithm performed well in several experiments and should prove to be a useful method in selecting features for classification problems.
RF MEMS Actuated Reconfigurable Reflectarray Patch-Slot Element
(2008)

This paper describes the design of a reconfigurable reflectarray element using commercially available radio frequency micro-electromechanical system (RF MEMS) switches. The element consists of a microstrip patch on the top surface and a slot with an actuated variable length in the ground plane. RF MEMS switches are mounted on the slot to electronically vary the slot length by actuating the switches and thus obtaining the desired phase response. Waveguide measurements and high frequency structure simulator (HFSS) simulations are used to characterize the reflectarray element. The four MEMS switches element gives 10 independent states with a phase swing of 150 deg and a loss variation from 0.4 dB to 1.5 dB at 2 GHz (more switches can provide larger phase shift). The loss is mainly attributed to the dielectric loss and the conductor loss, which occur due to the relatively strong electric fields in the substrate region below the patch and the large currents on the top surface of the patch, respectively, close to the patch resonance. Detailed analysis is performed to characterize the effect of the switches by taking into consideration the switch model and wire bonding effects.
Generalizing and Improving Weight Initialization
(2016)

We propose a new weight initialization suited for arbitrary nonlinearities by generalizing previous weight initializations. The initialization corrects for the influence of dropout rates and an arbitrary nonlinearity’s influence on variance through simple corrective scalars. Consequently, this initialization does not require computing mini-batch statistics nor weight pre-initialization. This simple method enables improved accuracy over previous initializations, and it allows for training highly regularized neural networks where previous initializations lead to poor convergence.
Probabilistic Inductive Logic Programming
(2008)

Probabilistic inductive logic programming aka. statistical relational learning addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with machine learning and first order and relational logic representations. A rich variety of different formalisms and learning techniques have been developed. A unifying characterization of the underlying learning settings, however, is missing so far. In this chapter, we start from inductive logic programming and sketch how the inductive logic programming formalisms, settings and techniques can be extended to the statistical case. More precisely, we outline three classical settings for inductive logic programming, namely learning from entailment, learning from interpretations, and learning from proofs or traces, and show how they can be adapted to cover state-of-the-art statistical relational learning approaches.
Intuitive visualization of Pareto Frontier for multi-objective optimization in n-dimensional performance space
(2004)

A visualization methodology is presented in which a Pareto Frontier can be visualized in an intuitive and straightforward manner for an n-dimensional performance space. Based on this visualization, it is possible to quickly identify ‘good’ regions of the performance and optimal design spaces for a multi-objective optimization application, regardless of space complexity. Visualizing Pareto solutions for more than three objectives has long been a significant challenge to the multi-objective optimization community. The Hyper-space Diagonal Counting (HSDC) method described here enables the lossless visualization to be implemented. The proposed method requires no dimension fixing. In this paper, we demonstrate the usefulness of visualizing n-f space (i.e. for more than three objective functions in a multiobjective optimization problem). The visualization is shown to aid in the final decision of what potential optimal design point should be chosen amongst all possible Pareto solutions.
MATCHINGS AND PAIRED DOMINATION IN FUZZY GRAPHS USING STRONG ARCS
(2018)

The concepts of covering and matching in fuzzy graphs using strong arcs are introduced and obtained the relationship between them analogous to Gallai’s results in graphs. The notion of paired domination in fuzzy graphs using strong arcs is also studied. The strong paired domination number γspr of complete fuzzy graph and complete bipartite fuzzy graph is determined and obtained bounds for the strong paired domination number of fuzzy graphs. An upper bound for the strong paired domination number of fuzzy graphs in terms of strong independence number is also obtained.
Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM)
(2017)

Purpose – The integration of relevant antecedents into TAM would lead to better understanding of the decision factors which act as enablers for the adoption of internet banking. The purpose of the paper is to determine the influence of the antecedents subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk, trialability and government support on the existing constructs of the technology acceptance model (TAM) and to test measurement invariance and the moderating effect of the demographic variables on the relationship between the latent constructs used in this augmented TAM. Design/methodology/approach – A survey questionnaire was administered on internet banking users and a total of 300 responses were collected. A two-step approach suggested by Hair et al. (2006) and Schumacker and Lomax (2004) was used in this study. The proposed model was assessed using the confirmatory factor analysis approach. The structural model was then tested in order to establish nomological validity. The data based on four demographic dimensions gender, age, income, education were divided into two groups for each of these demographic dimensions. The invariance test was first performed on the measurement model and then on the structural model. The measurement model and structural model were subjected to tests of equivalence of parameters across groups. Findings – To a large extent the results of the study supports the proposed model and thereby contributes to understand the influence of subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk and government support on internet banking adoption. The predictor variables in the augmented TAM were able to explain 29.9 per cent of the variance in the actual usage of internet banking as compared to the TAM which was able to explain only 26.5 per cent variance in the actual usage of internet banking. A significant difference in the relationship between the different constructs of the model was observed when the model was subjected to multi-group invariance testing. Research limitations/implications – The study suffers from the same limitations as most other studies involving TAM. In this study self-reported measures about the usage were taken as the actual usage. The findings of the study can be of use to marketers for target-specific marketing by customizing the marketing campaign focussing on the factors that were found to be strong influencers leading to the usage of internet banking for each target audience. Originality/value – The main challenge in this study was to develop the conceptual model for the internet banking adoption by extending the TAM and to get a robust theoretical support from the extant literature for the relevant factors along with their relationship to uncover new insights about factors responsible for the internet banking adoption. The augmented model had an improved predictive capability and explanatory utility.
FeUdal Networks for Hierarchical Reinforcement Learning
(2017)

We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan and Hinton, and gains power and efficacy by decoupling end-to-end learning across multiple levels – allowing it to utilise different resolutions of time. Our framework employs a Manager module and a Worker module. The Manager operates at a lower temporal resolution and sets abstract goals which are conveyed to and enacted by the Worker. The Worker generates primitive actions at every tick of the environment. The decoupled structure of FuN conveys several benefits – in addition to facilitating very long timescale credit assignment it also encourages the emergence of sub-policies associated with different goals set by the Manager. These properties allow FuN to dramatically outperform a strong baseline agent on tasks that involve long-term credit assignment or memorisation. We demonstrate the performance of our proposed system on a range of tasks from the ATARI suite and also from a 3D DeepMind Lab environment.
Parameter Space Noise for Exploration
(2018)

Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent’s parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both offand on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually.
BOA: the Bayesian optimization algorithm
(1999)

In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed. To estimate the distribution, techniques for modeling multivariate data by Bayesian networks are used. The proposed algorithm identifies, reproduces and mixes building blocks up to a specified order. It is independent of the ordering of the variables in the strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm. However, prior information is not essential. Preliminary experiments show that the BOA outperforms the simple genetic algorithm even on decomposable functions with tight building blocks as a problem size grows.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
(2016)

Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. One of the key difficulties is insufficient exploration, resulting in an agent being unable to learn robust policies. Intrinsically motivated agents can explore new behavior for their own sake rather than to directly solve external goals. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchicalDQN (h-DQN), a framework to integrate hierarchical action-value functions, operating at different temporal scales, with goal-driven intrinsically motivated deep reinforcement learning. A top-level q-value function learns a policy over intrinsic goals, while a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications, such as functions over entities and relations. This provides an efficient space for exploration in complicated environments. We demonstrate the strength of our approach on two problems with very sparse and delayed feedback: (1) a complex discrete stochastic decision process with stochastic transitions, and (2) the classic ATARI game – ‘Montezuma’s Revenge’.
Cell biology of protein misfolding: The examples of Alzheimer's and Parkinson's diseases
(2004)

The salutary intersection of fundamental cell biology with the study of disease is well illustrated by the emerging elucidation of neurodegenerative disorders. Novel mechanisms in cell biology have been uncovered through disease-orientated research; for example, the discovery of presenilin as an intramembrane aspartyl protease that processes many diverse proteins within the lipid bilayer. A common theme has arisen in this field: normally-soluble proteins accumulate, misfold and oligomerize, inducing cytotoxic effects that are particularly devastating in the post-mitotic milieu of the neuron.
Topological Data Analysis of Biological Aggregation Models
(2015)

We apply tools from topological data analysis to two mathematical models inspired by biological aggregations such as bird flocks, fish schools, and insect swarms. Our data consists of numerical simulation output from the models of Vicsek and D'Orsogna. These models are dynamical systems describing the movement of agents who interact via alignment, attraction, and/or repulsion. Each simulation time frame is a point cloud in position-velocity space. We analyze the topological structure of these point clouds, interpreting the persistent homology by calculating the first few Betti numbers. These Betti numbers count connected components, topological circles, and trapped volumes present in the data. To interpret our results, we introduce a visualization that displays Betti numbers over simulation time and topological persistence scale. We compare our topological results to order parameters typically used to quantify the global behavior of aggregations, such as polarization and angular momentum. The topological calculations reveal events and structure not captured by the order parameters.
The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM
(2016)

We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems. SAPALM is the first asynchronous parallel optimization method that provably converges on a large class of nonconvex, nonsmooth problems. We prove that SAPALM matches the best known rates of convergence — among synchronous or asynchronous methods — on this problem class. We provide upper bounds on the number of workers for which we can expect to see a linear speedup, which match the best bounds known for less complex problems, and show that in practice SAPALM achieves this linear speedup. We demonstrate state-of-the-art performance on several matrix factorization problems.
Characterization of Silicided Polysilicon Fuse Implemented in 65nm Logic CMOS Technology
(2006)

NiSi electrically programmable fuses (eFUSE) were fabricated and investigated using 65 nm logic CMOS technology. The optimization of fuse program was achieved by analyzing electrical and physical responses of fuse bits for various conditions. Controlled electromigration of Ni during fuse program was identified as a key factor in achieving reliably high post-program fuse resistance.
Prediction of Microwave Absorption Behavior of Grading Honeycomb Composites Based on Effective Permittivity Formulas
(2015)

Microwave absorbing honeycomb composites with grading coating thickness are prepared by impregnation of aramid paper frame within water solutions of conductive carbon blacks. Based on effective medium method, grading impedance in the composites can be theoretically expressed by a linear approximation of the inversely tapered effective permittivity at wave propagation direction, which provides a feasible way to optimize the design of microwave absorbing honeycomb structures. The consistent result of calculations and experimental measurements for positive impedance grading design confirms the broadband absorption effect in high frequencies.
New support vector algorithms with parametric insensitive/margin model
(2010)

In this paper, a modification of v-support vector machines (v-SVM) for regression and classification is described, and the use of a parametric insensitive/margin model with an arbitrary shape is demonstrated. This can be useful in many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value x. Like the previous v-SVM, the proposed support vector algorithms have the advantage of using the parameter 0 < or = v < or = 1 for controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of training errors and a lower bound on the fraction of support vectors. The algorithms are analyzed theoretically and experimentally.
The feeling of effort during mental activity
(2018)

The feeling of effort is familiar to most, if not all, humans. Prior research shows that the feeling of effort shapes judgments (e.g., of agency) and decisions (e.g., to quit the current task) in various ways, but the proximal causes of the feeling of effort are not well understood. In this research, I address these proximal causes. In particular, I conducted two preregistered experiments in which participants performed a difficult vs. easy cognitive task, while I measured effort-related phenomenology (feeling of effort) and physiology (pupil dilation) on a moment-to-moment basis. In both experiments, difficult tasks increased the feeling of effort; however, this effect could not be explained by concurrent increases in physiological effort. To explain these findings, I suggest that the feeling of effort during mental activity stems from the decision to exert physiological effort, rather than from physiological effort itself.
Temporal and Real-Time Databases: A Survey
(1995)

• a short biography of John Doe. – born on April 3rd, 1975 in Smallville. His birth was registered on April 4th, 1975. – He went to live on his own in Bigtown. Although he moved out on August 26th, 1994, he forgot to register the change of address officially. – He updated his record on December 27, 1994. – John Doe was accidentally hit by a truck on April 1st, 2001. The coroner reported his date of death on the next day.
Conflict-Aware Event-Participant Arrangement and Its Variant for Online Setting
(2016)

With the rapid development of Web 2.0 and Online To Offline (O2O) marketing model, various <italic>online <underline> e</underline>vent-<underline>b</underline>ased <underline>s</underline>ocial <underline>n</underline>etwork<underline>s </underline></italic> (EBSNs) are getting popular. An important task of EBSNs is to facilitate the most satisfactory event-participant arrangement for both sides, i.e., events enroll more participants and participants are arranged with personally interesting events. Existing approaches usually focus on the arrangement of each single event to a set of potential users, or ignore the conflicts between different events, which leads to infeasible or redundant arrangements. In this paper, to address the shortcomings of existing approaches, we first identify a more general and useful event-participant arrangement problem, called <italic><underline>G</underline>lobal <underline>E</underline> vent-participant <underline>A</underline>rrangement with <underline>C</underline>onflict and <underline>C</underline> apacity</italic> (<inline-formula><tex-math notation="LaTeX">$GEACC$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="chen-ieq1-2565468.gif"/></alternatives></inline-formula>) problem, focusing on the conflicts of different events and making event-participant arrangements in a global view. We find that the GEACC problem is NP-hard due to the conflicts among events. Thus, we design two approximation algorithms with provable approximation ratios and an exact algorithm with pruning technique to address this problem. In addition, we propose an online setting of GEACC, called OnlineGEACC, which is also practical in real-world scenarios. We further design an online algorithm with provable performance guarantee. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.